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A Novel Predictive Analysis and Classification of Land Subsidence Vulnerability Mapping based on GIS using Hybrid Optimized Machine Learning Techniques and computer Vision

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Procedia computer science 2024年 233卷 343-352页

作者： Senthil G. A R. Prabha Arun M A. Sahaya Anselin Nisha S. Prabu Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Chennai India Assistant Professor Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Professor Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Assistant Professor Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Land subsidence is a natural disaster caused by the extraction of the earth's material strength on the surface that causes the earth's surface to settle and sink. Land subsidence can also result in significant financial losses since it causes structural damage and expensive maintenance costs for roads, trains, barriers, pipelines, ground water, oil, natural gas, mines, and buildings. The Geographic information System (GIS) and Google Earth Platform (GEP) can be used to map this land subsidence. This provides a comprehensive view of land pattern subsidence and its interactions with other geographical elements. It applies to both progressive subsidence caused by fluid extraction (groundwater, oil, or natural gas) and rapid surface subsidence caused by the collapse of an underground mine. Risk mitigation is the most effective way to deal with the experimental model. This research study focuses on the use of Predictive Data Analysis (PDA), a powerful environment in data analytics that provides a set of tools that make it easier to evaluate optimal models. The proposed model makes use of a dataset that includes specific land subsidence circumstances as input. The prediction result is achieved using a hybrid machine learning technique that combines Stochastic Gradient Descent (SGD) with Long Short-Term Memory (LSTM) and is compared to K-Nearest Neighbor (KNN), Generative Adversarial Network (GAN), and Artificial Neural Network (ANN). The proposed system employs LSTM and SGD as optimizers, resulting in a hybrid model that improves accuracy. Among the four algorithms investigated the LSTM-SGD network has the highest accuracy (97%), followed by KNN (96%), GAN (90%), and ANN (89%). Due to their ability to capture complicated nonlinear correlations in data and manage temporal dependencies, optimized hybrid ML models LSTM-SGD and KNN models appear to be particularly well-suited for land subsidence prediction.

关键词： Land Subsidence Predictive Data Analysis (PDA) Geographic information System Machine Learning computer Cision Susceptibility Mapping Stochastic Gradient Descent (SGD) Generative Adversarial Network (GAN) Long Short-Term Memory (LSTM) K-Nearest Neighbor (KNN) Artificial Neural Network (ANN) Google Earth Platform

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Unleashing Facial Expression Recognition for Stress Detection Using Deep CNN Model.

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Procedia computer science 2025年 259卷 306-315页

作者： Geethu S Kumar Jo Cheriyan Aparna N Swathy J Research Scholar Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai- 600 119 Tamilnadu India Assistant Professor Department of Information Science and Engineering Cambridge Institute of Technology Bangalore-560 036 Karnataka India Assistant Professor Department of Computer Science and Engineering Saintgits College of Engineering Kottayam- 686 532 Kerala India Assistant Professor Department of Computer Science and Engineering Cambridge Institute of Technology Bangalore-560 036 Karnataka India

The technology and field of study known as Facial Emotion Recognition (FER) focuses on recognising and deciphering human facial expressions to ascertain emotions. It incorporates aspects of psychology, machine learning, and computer vision. Considering its ability to analyse facial expressions and spot signs of stress or emotional distress, FER plays a vital role in the detection of stress. Microexpressions are fleeting, uncontrollably expressed facial expressions that convey true feelings. These ephemeral expressions, which are frequently linked to stress, can be captured by FER systems. With its capacity to manage intricate patterns and variances in face expressions, FER utilising deep learning has grown in popularity. A novel FER system was designed to overcome issues that plague current FER systems, such as imbalanced datasets and robustness to noisy inputs. Large datasets are handled effectively by FER employing Deep CNN because of its architecture, which is built for high-dimensional input like images. Leveraging FER using Deep CNN achieves notable performance improvements, attaining an accuracy of 95.65 % and F1 score of 94.02%. Deep learning-based FER marks a substantial advancement in our ability to recognize and decipher facial expressions that convey emotions. FER technology has the ability to revolutionize several fields by improving intuition and sensitivity to human emotions through further development and thoughtful assessment of ethical ramifications. FER systems will advance in sophistication as science and technology develop, offering more profound understanding of human emotional states and promoting improved human-machine interactions.

关键词： Stress PPG Conv-XGBoost deep learning mental health

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Design of a Multiple Phased Convolution Model for Pre-emption of Breast Cancer via Extensive Learning Operations (MPC2EL)

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Procedia computer science 2024年 233卷 638-650页

作者： Pravin Malve Dr. Vijay Gulhane Research Scholar Department of Computer Science & Engineering Sipna College of Engineering & Technology Amravati 444607 India Professor Department of Information Technology Sipna College of Engineering & Technology Amravati 444607 India

Breast cancer diagnosis from mammographic scans is a complex challenge, demanding accurate and efficient methodologies. Existing approaches often rely on time-consuming manual feature extraction or lack scalability when employed with diverse clinical image sets. To address these limitations, this paper presents an innovative solution: the MPC2EL (Multiple Phased Convolution model for Pre-emption of Breast Cancer via Extensive Learning Operations). The core problem tackled by this research is the need for improved breast cancer detection, especially concerning scalability, accuracy, and practicality. To achieve this, MPC2EL leverages extensive learning operations and multiple phased convolutions to process extensive clinical datasets containing both cancerous and benign images. Our novel approach begins with the conversion of these images into multimodal feature sets, encompassing frequency, cosine, wavelet, and Gabor domains. These feature sets enable enhanced classification of images into different cancer stages. The proposed methodology lies in a Dual 1D Convolutional Neural Network (D1D CNN) architecture, capable of categorizing images initially into malignant and benign types and subsequently refining classification for malignant images. Experimental results reveal a substantial 5.9% increase in classification accuracy and a 4.5% improvement in recall compared to conventional deep learning methods. Moreover, the model demonstrates lower processing delay and higher precision, making it a promising candidate for real-world clinical deployment.

关键词： Cancer Mammogram Multiple Phased CNN Gabor Cosine

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Collaborative Defense Strategies: AI, ML, and Cybersecurity: A Comprehensive Study 12th

Collaborative Defense Strategies: AI, ML, and Cybersecurity:...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Borah, Samarjeet Dhabliya, Dharmesh Dhage, Prashant Shukla, Jyoti Rai, Sumit Kumar Bhattacharya, Saurabh Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Information Technology Vishwakarma Institute of Information Technology Maharashtra Pune India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Maharashtra Pimpri Pune India Assistant Professor SCSE Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819601462

In the fast-changing field of cybersecurity, the rise of advanced threats requires new and creative defense strategies. This study thoroughly examines collaborative defense strategies, focusing on the utilization of artificial intelligence (AI) and machine learning (ML) techniques. The study aims to evaluate the effectiveness of five prominent machine learning algorithms (random forest, support vector machine (SVM), gradient boosting, isolation forest, and one-class SVM) in the field of cybersecurity. The assessment is carried out utilizing the NSL-KDD dataset, which is a standard in the industry. The study commences by investigating the origins of collaborative defense, analyzing its historical development and fundamental principles. The text explains the importance of AI in collaborative defense, highlighting machine learning algorithms and natural language processing as essential elements. The study explores the uses of artificial intelligence, specifically in identifying, averting, and reacting to threats. A substantial part of the research focuses on evaluating the effectiveness of the five chosen machine learning algorithms. The evaluation includes predictive analysis to forecast cyber threats and proactive defense measures. The study examines how machine learning improves the sharing of threat intelligence by facilitating real-time information exchange between collaborating entities. The challenges and considerations related to incorporating AI and ML into cybersecurity are examined, emphasizing ethical implications, bias, fairness, transparency, and accountability. Case studies and success stories from real-world scenarios showcase examples of successful collaborative defense strategies applied in various industries and sectors. The study evaluates and contrasts the random forest, SVM, gradient boosting, isolation forest, and one-class SVM algorithms based on performance metrics like accuracy, precision, recall, and F1 score, utilizing the NSL-KDD dataset. T

关键词： Adversarial machine learning

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Biometric-Based Access Control Systems with Robust Facial Recognition in IoT Environments 3

Biometric-Based Access Control Systems with Robust Facial Re...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Satish Babu, B.V. Manoranjini, J. Changala, Ravindra Aarif, Mohd Mary, S. Suma Christal Raj, I Infant Prasad v Potluri Siddhartha Institute of Technology Department of It Vijayawada India Chaitanya Bharathi Institute of Technology Department of Ai & Ml Hyderabad India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Associate Editor-Publishing Global Research Network Noida India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

The integration of biometric-based access control systems with robust facial recognition technology within Internet of Things (IoT) environments holds paramount significance in addressing contemporary security challenges. The novelty of the research endeavours to contribute to this critical area by investigating and implementing a novel approach, employing Convolutional Neural Networks (CNN) for facial classification within the biometric framework. The significance of this study lies in its potential to enhance security measures in IoT environments, ensuring reliable and efficient access control. Previous attempts at integrating facial recognition into IoT have encountered notable challenges, including suboptimal accuracy, vulnerability to adversarial attacks, and scalability issues. This research builds upon these prior issues by introducing CNN for facial classification, leveraging its capabilities in image processing and pattern recognition to achieve enhanced accuracy and resilience against potential security threats. The novelty of the study is underscored by the application of CNN within the biometric context of IoT, offering a cutting-edge solution tailored to the intricacies of interconnected devices. Experiments conducted on benchmark datasets showcase the promising capabilities of the proposed system, with face recognition accuracies reaching an impressive 98%. These results underscore the potential of this research to significantly advance the state-of-the-art in biometric-based access control, particularly within the context of IoT environments. The utilization of CNN introduces a pioneering element, contributing to the evolution of secure and efficient access control solutions in the era of IoT. © 2024 IEEE.

关键词： Biometrics

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Deep Learning-Based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 54-70页

作者： Zezario, Ryandhimas E. Fu, Szu-Wei Chen, Fei Fuh, Chiou-Shann Wang, Hsin-Min Tsao, Yu National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei115024 Taiwan Microsoft VancouverBCV6B 5C6 Canada Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen518055 China Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan Chung Yuan Christian University Jointly Appointed Professor with the Department of Electrical Engineering Taoyuan32023 Taiwan

This study proposes a cross-domain multi-objective speech assessment model, called MOSA-Net, which can simultaneously estimate the speech quality, intelligibility, and distortion assessment scores of an input speech signal. MOSA-Net comprises a convolutional neural network and bidirectional long short-Term memory architecture for representation extraction, and a multiplicative attention layer and a fully connected layer for each assessment metric prediction. Additionally, cross-domain features (spectral and time-domain features) and latent representations from self-supervised learned (SSL) models are used as inputs to combine rich acoustic information to obtain more accurate assessments. Experimental results show that in both seen and unseen noise environments, MOSA-Net can improve the linear correlation coefficient (LCC) scores in perceptual evaluation of speech quality (PESQ) prediction, compared to Quality-Net, an existing single-Task model for PESQ prediction, and improve LCC scores in short-Time objective intelligibility (STOI) prediction, compared to STOI-Net, an existing single-Task model for STOI prediction. Moreover, MOSA-Net can be used as a pre-Trained model to be effectively adapted to an assessment model for predicting subjective quality and intelligibility scores with a limited amount of training data. Experimental results show that MOSA-Net can improve LCC scores in mean opinion score (MOS) predictions, compared to MOS-SSL, a strong single-Task model for MOS prediction. We further adopt the latent representations of MOSA-Net to guide the speech enhancement (SE) process and derive a quality-intelligibility (QI)-Aware SE (QIA-SE) approach. Experimental results show that QIA-SE outperforms the baseline SE system with improved PESQ scores in both seen and unseen noise environments over a baseline SE model. © 2014 IEEE.

关键词： Speech enhancement

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Optimizing Resource Allocation in Healthcare Facilities Through IoT and Machine Learning Predictive Analysis 3rd

Optimizing Resource Allocation in Healthcare Facilities Thro...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Dankan Gowda, V. Sharma, Avinash Mukherjee, Saptarshi Rahman, Mirzanur Barshe, Shweta B. Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Professor Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Punjab Mohali140307 India Makaut Maulana Abul Kalam Azad University of Technology West Bengal Nadia Kolkata India Department of Information Technology Gauhati University Assam Guwahati India Department of Computer Engineering Bharati Vidyapeeth College of Engineering Mumbai Navi India

ISBN: (纸本)9789819751457

In the evolving landscape of health care, optimal resource allocation emerges as a critical determinant of patient outcomes and facility efficiency. This study explores the integration of Internet of Things (IoT) devices and machine learning (ML) predictive analysis in healthcare facilities to optimize resource allocation. With the omnipresence of IoT devices and the analytic prowess of ML, our research aims to develop a model that can predict real-time resource demands, ranging from patient care equipment to manpower distribution. Drawing data from wearable sensors, embedded devices, and facility infrastructure, the proposed ML model demonstrates significant improvement in predicting short-term resource needs. Preliminary results indicate a reduction in resource wastage, enhanced patient care, and increased operational efficiency in healthcare facilities that incorporated the proposed system. However, the research also acknowledges potential limitations, including data security concerns and system scalability. The findings presented highlight the transformative potential of IoT and ML in revolutionizing resource management in health care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data security Embedded devices Internet of Things (IoT) Machine learning (ML) Operational efficiency Wearable sensors

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A new algorithm for detection of nodes failures and enhancement of network coverage and energy usage in wireless sensor networks

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Materials Today: Proceedings 2023年 80卷 1717-1722页

作者： Satyanarayana, P. Mahalakshmi, T. Sivakami, R. Alahmari, Saad Ali Rajeyyagari, Sivaram Asadi, Srinivasulu ECE V. R. Siddhartha Engineering College Vijayawada India Department of ECE Prasad V Potluri Siddhartha Institute of Technology India Associate Professor CSE Sona College of Technology TN Salem 5 India Department of Computer Science Shaqra University Saudi Arabia Data Analytics Research Laboratory BlueCrest University College Monrovia Liberia

Present natural lives shows rising attention in various applications of the wireless sensor networks (WSNs). Significant applications are isolated and stiff regions where human involvement is hazardous or unfeasible. Some examples are space investigation, battlefield observation, costal, and edge security;nowadays, such networks are needful in several manufacturing and customer applications. A WSN is a device system, indicated as nodes;these nodes intellect the atmosphere and transfer the details collected from the ground through a wireless connection. Statistics are sent, probably via many hops, to a target that can use it nearby or is linked to various networks using the gateway. The sensor nodes might be stationary or changeable. Every node has clear data about the neighborhood or not. Communication over WSN over ecological hazards is a key issue. These constraints might impact the performance of the sensors/direction finding protocols and resource utilization;thus, they may show the way to the node crash situation, i.e., software/hardware breakdown, safety threats, extreme power utilization, etc. It is essential to examine the collision of breakdown over system performance. In the projected method, relay nodes are used as secure nodes for the positioned sensor nodes. The projected method is separated into two phases: the intra segregation stage and inter segregation stage. In the intra segregation stage, the unnecessary nodes reach nearer to the segregation border, and dispersion slowly enlarges the coverage. The projected model is examined through simulations and compares the results with existing methods. © 2021

关键词： Sensor nodes

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Feature Extraction, Essential Dimension Estimation and Dimension Reduction Method of Colony Images

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-13页

作者： Sun, Wei-Zhong Guo, Fu-Jun Wang, Jie-Sheng Chen, Lin Wei, Dong Du, Xin-Feng A lecturer of School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China A postgraduate student of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China A professor of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China An associate professor of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

A colony is a group of microorganisms produced by the growth and reproduction of a single microorganism species. A method of colony image feature extraction, essential dimension estimation and dimension reduction based on digital image processing technology was proposed. Firstly, based on the HSI image color moments (first, second and third order moments), nine color characteristics of the colony images were extracted. Based on gray-level co-occurrence matrix (GLCM), twenty texture features of colony images were obtained. Then, three essential dimension estimation methods, namely, the correlation dimension estimator, the maximum likelihood estimator and the packing numbers estimator, are used to estimate the dimension of the inner low-dimensional structure of the high-dimensional data of the colony images. The results of different dimension estimators were combined with six different dimension reduction techniques (PCA, LDA, MDS, ISOMAP, SNE and NCA) respectively to form 18 different data dimension reduction methods. Based on the distance criterion function, an optimal dimension reduction method for the high-dimensional feature data of the colony images corresponding to the maximum distance criterion function was obtained. © 2021. All Rights Reserved.

关键词： Feature extraction

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Rice Category Identification through Deep Transfer Learning Features and Machine Learning Classifiers: An Intelligent Approach

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第7期51卷 765-784页

作者： Terlapu, Panduranga Vital Prasan, U.D. Kumar, T. Ravi Bendalam, Vijaya Pappu, Sasibhushana Rao Rao, M. Jayanthi Mohitha, Maddula Ratna Jayaram, D. Dept. of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Sasibhushana Rao Pappu is an Assistant Professor of Dept. of Department of Computer Science and Engineering GITAM Andhra Pradesh Visakhapatnam India Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Department of Information Technology Chaitanya Bharathi Institute of Technology Gandipet Telangana Hyderabad500075 India

Rice category identification by image analysis is essential to ensure the quality and safety of rice production. In this study, we propose an intellectual approach to improve rice category identification using deep transfer learning features and machine learning classifiers. Specifically, we extracted features from three pre-trained models (Inception V3, VGG-19 and VGG-16) using transfer learning techniques. These were used as inputs to train MultiLayerPerceptron (MLP) and support vector machine (SVM) classifiers. The results of our experiments show that the proposed strategic results achieve high accuracy in identifying rice categories. The SVM (polynomial kernel) achieves the second-highest accuracy among all models and features, with an accuracy of 0.9948 using the VGG-19 and 0.9912 using Inception V3. The MLP classifier with (30 30) hidden layers achieve the first high accuracy, with an accuracy of 0.9972 (99.72%) using VGG-19 features. The results also show that the choice of deep transfer learning model and machine learning (ML) classifier can significantly affect the accuracy of rice category identification. Among the three pretrained models, VGG-19 features consistently perform the best, followed by Inception V3 and VGG-16. The choice of MLP hidden layer size also affects the accuracy, with 30 HL neurons achieving the best performance. Our proposed approach using deep TL features and ML classifiers shows promising results in improving rice category identification. Our study provides valuable insights into optimizing ML models for agricultural image analysis. © (2024) International Association of Engineers. All rights reserved.

关键词： Support vector machines

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